Observing the distributions and chemistry of major air pollutants (O3 and PM2.5) from space: trends, uncertainties, and health implications

Ambient exposure to fine particulate matter (PM2.5) and ground-level ozone (O3) is identified as a leading risk factor for global disease burden. A major limitation to advancing our understanding of the cause and impacts of air pollution is the lack of observations with the spatial and temporal resolution needed to observe variability in emission, chemistry and population exposure. Satellite remote sensing, which fills a spatial gap in ground-based networks, is playing an increasingly important role in atmospheric chemistry. This thesis exploits satellite remote sensing observations to: (1) estimate human exposure to PM2.5 from remotely sensed aerosol optical properties; (2) identify the chemical regimes of surface O3 formation using satellite observations of O3 precursors. In the first part, we use a forward geophysical approach to derive PM2.5 distributions from satellite AOD at 1 km2 resolution over the northeastern US by applying relationships between PM2.5 and AOD simulated from a regional air quality model (CMAQ). We use multi-platform ground, airborne and radiosonde measurements to quantify multiple sources of uncertainties in the satellite-derived PM2.5. We find that uncertainties in satellite-derived PM2.5 are largely attributed to the varying relationship between PM2.5 and AOD that depends on the aerosol vertical distribution, speciation, aerosol optical properties and ambient relative humidity. To assess the value of remote sensing to improve PM2.5 exposure estimate, we compile multiple PM2.5 products that include information from remote sensing, ground-based observations and models. Evaluating these products using independent observations, we find the inclusion of satellite remote sensing improves the representativeness of surface PM2.5 mostly in the remote areas with sparse monitors. Due to the success of emission control, PM2.5-related mortality burden over NYS decreased by 67% from 8410 (95% confidence interval (CI): 4, 570 – 12, 400) deaths in 2002 to 2750 (95% CI: 700 – 5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM2.5 mortality burden due to the choice of PM2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function. The second part of the thesis focuses on ground-level O3. O3 production over urban areas is non-linearly dependent on the availability of its precursors: nitrogen oxides (NOx) and volatile organic compounds (VOCs). A major challenge in lowering ground-level O3 in urban areas is to determine the limiting species for O3 production (NOx-limited or VOC-limited). We use satellite observations of NO2 and HCHO to infer the relative abundance of NOx versus VOCs, thus to identify the O3 chemical regime. We first use a global chemical transport model (GEOS-Chem) to evaluate the uncertainties of using satellite-based HCHO/NO2 to infer O3 sensitivity to precursor emissions. Next, we directly connect this space-based indicator, retrieved consistently from three satellite instruments, to spatiotemporal variations in O3 recorded by on-the-ground monitors from 1996 to 2016. The nationwide emission reduction has led the O3 formation over U.S. urban areas to shift from VOC-limited to NOx-limited regime. Urban O3 monitors reveal trends consistent with this regime transition. Nonetheless, it is a major challenge for these retrievals to accurately depict day-to-day variability within urban cores. TROPOspheric Monitoring Instrument (TROPOMI) which launched in 2017, offers an unprecedented view to infer O3 chemistry at fine spatial and temporal scales. As an example, we use TROPOMI HCHO/NO2 to identify short-term changes in O3 sensitivity during the California Camp Fire. We find that the emissions from wildfires lead to NOx-saturated ozone formation near the fire source but NOx -limited conditions downwind. This thesis bridges basic research in atmospheric chemistry, which advances the state-of-science related to O3 and PM2.5 pollution from urban to global scales, and applied research in air quality management and public health, by quantifying the health benefits of emission control, and informs policymakers on which emission reductions to focus so as to maximize the cost-effectiveness of pollution controls. We show how space-based measurements can complement in situ networks and model simulations by providing information on the spatial heterogeneity and temporal evolution of PM2.5 exposure and O3 chemical regimes, which will lay the scientific foundation for interpreting future products retrieved from upcoming geostationary platforms

Using satellite observed formaldehyde (HCHO) and nitrogen dioxide (NO2) as an indicator of ozone sensitivity in a SIP

Although State Implementation Plans (SIPs) typically rely on observations from ground-based networks and regulatory models, satellite data is increasingly available to state agencies and can also inform and supplement state implementation plans to improve air quality. An advantage of satellite data is that it provides information for a broader area than sampled by ground-based networks. This document provides examples and guidance for using satellite products of formaldehyde (HCHO) and nitrogen dioxide (NO2) to inform ground-level ozone sensitivity to emissions of nitrogen oxides (NOx) versus volatile organic compounds (VOC) in state implementation plans. Analysis of changes in ozone sensitivity over periods where emission controls have been implemented can provide insights into the efficacy of those past strategies and the likely efficacy of proposed future emission control programs

Market pressure or regulatory pressure? U.S. small bank pre-emptive IT investment to data privacy regulation

We assess small banks’ responses to announcements of state-level proposals of Privacy Protection Acts (PPAs). Employing a Difference-in-Differences framework, we uncover the proactive actions taken by U.S. small banks in anticipation of these proposals. Our findings reveal that the announcement of PPA proposals leads to a 35.46% increase in IT investment by U.S. small banks, primarily driven by market pressure, with regulatory pressure playing a more limited role. Particularly, evidence suggests that banks with greater competitive threats from their rivals are motivated to enhance their IT investments due to market pressures. However, our research also finds that this surge in IT investment does not immediately translate into benefits for small banks

Masked and Permuted Implicit Context Learning for Scene Text Recognition

Scene Text Recognition (STR) is difficult because of the variations in text styles, shapes, and backgrounds. Though the integration of linguistic information enhances models' performance, existing methods based on either permuted language modeling (PLM) or masked language modeling (MLM) have their pitfalls. PLM's autoregressive decoding lacks foresight into subsequent characters, while MLM overlooks inter-character dependencies. Addressing these problems, we propose a masked and permuted implicit context learning network for STR, which unifies PLM and MLM within a single decoder, inheriting the advantages of both approaches. We utilize the training procedure of PLM, and to integrate MLM, we incorporate word length information into the decoding process and replace the undetermined characters with mask tokens. Besides, perturbation training is employed to train a more robust model against potential length prediction errors. Our empirical evaluations demonstrate the performance of our model. It not only achieves superior performance on the common benchmarks but also achieves a substantial improvement of $9.1\%$ on the more challenging Union14M-Benchmark

Model Will Tell: Training Membership Inference for Diffusion Models

Diffusion models pose risks of privacy breaches and copyright disputes, primarily stemming from the potential utilization of unauthorized data during the training phase. The Training Membership Inference (TMI) task aims to determine whether a specific sample has been used in the training process of a target model, representing a critical tool for privacy violation verification. However, the increased stochasticity inherent in diffusion renders traditional shadow-model-based or metric-based methods ineffective when applied to diffusion models. Moreover, existing methods only yield binary classification labels which lack necessary comprehensibility in practical applications. In this paper, we explore a novel perspective for the TMI task by leveraging the intrinsic generative priors within the diffusion model. Compared with unseen samples, training samples exhibit stronger generative priors within the diffusion model, enabling the successful reconstruction of substantially degraded training images. Consequently, we propose the Degrade Restore Compare (DRC) framework. In this framework, an image undergoes sequential degradation and restoration, and its membership is determined by comparing it with the restored counterpart. Experimental results verify that our approach not only significantly outperforms existing methods in terms of accuracy but also provides comprehensible decision criteria, offering evidence for potential privacy violations.Comment: 18 pages, 6 figures, 7 table

OPT: One-shot Pose-Controllable Talking Head Generation

One-shot talking head generation produces lip-sync talking heads based on arbitrary audio and one source face. To guarantee the naturalness and realness, recent methods propose to achieve free pose control instead of simply editing mouth areas. However, existing methods do not preserve accurate identity of source face when generating head motions. To solve the identity mismatch problem and achieve high-quality free pose control, we present One-shot Pose-controllable Talking head generation network (OPT). Specifically, the Audio Feature Disentanglement Module separates content features from audios, eliminating the influence of speaker-specific information contained in arbitrary driving audios. Later, the mouth expression feature is extracted from the content feature and source face, during which the landmark loss is designed to enhance the accuracy of facial structure and identity preserving quality. Finally, to achieve free pose control, controllable head pose features from reference videos are fed into the Video Generator along with the expression feature and source face to generate new talking heads. Extensive quantitative and qualitative experimental results verify that OPT generates high-quality pose-controllable talking heads with no identity mismatch problem, outperforming previous SOTA methods.Comment: Accepted by ICASSP202

FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions

One-shot talking head generation has received growing attention in recent years, with various creative and practical applications. An ideal natural and vivid generated talking head video should contain natural head pose changes. However, it is challenging to map head pose sequences from driving audio since there exists a natural gap between audio-visual modalities. In this work, we propose a Flow-guided One-shot model that achieves NaTural head motions(FONT) over generated talking heads. Specifically, the head pose prediction module is designed to generate head pose sequences from the source face and driving audio. We add the random sampling operation and the structural similarity constraint to model the diversity in the one-to-many mapping between audio-visual modality, thus predicting natural head poses. Then we develop a keypoint predictor that produces unsupervised keypoints from the source face, driving audio and pose sequences to describe the facial structure information. Finally, a flow-guided occlusion-aware generator is employed to produce photo-realistic talking head videos from the estimated keypoints and source face. Extensive experimental results prove that FONT generates talking heads with natural head poses and synchronized mouth shapes, outperforming other compared methods.Comment: Accepted by ICME202